Medical assistance in dying: A political issue for nurses and nursing in Canada
Bibliographic record
Abstract
Death and dying are natural phenomena embedded within complex political, cultural and social systems. Nurses often practice at the forefront of this process and have a fundamental role in caring for both patients and those close to them during the process of dying and following death. While nursing has a rich tradition in advancing the palliative and end-of-life care movement, new modes of care for patients with serious and irremediable medical conditions arise when assisted death is legalized in a particular jurisdiction. In early 2015, the Supreme Court of Canada released its landmark decision Carter v. Canada (Attorney General) ('Carter'), which legalized physician-assisted suicide in particular clinical situations. The new law provided the broad national framework for Medical Assistance in Dying (MAiD) in Canada but, once the law was passed, provincial and territorial governments and health professional regulatory bodies each had to undertake a process of developing policies, procedures and processes to guide MAiD-related practice specific to their jurisdiction. In this paper, we begin to examine the political ramifications and professional tensions arising from MAiD for nurses and nursing, focusing specifically upon the impacts for registered nurses. We identify how variations in the provincial and territorial literature and regulatory guidelines across Canada have given rise to role confusion and uncertainty among some registered nurses and how this may potentially impact patient care. We then continue to highlight the need for greater political activism among nurses to foster greater clarity in nursing roles in MAiD and to advocate for improved supports for patients and those close to them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.043 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".